Students are more likely to cheat under intense pressure or when they rationalize the behavior

30 Jul 2026

Technology has become a big part of education, making learning more convenient and flexible. However, it has also made cheating easier. Students can now use multiple devices, messaging platforms, and even artificial intelligence tools to get answers during online tests or assignments. To help prevent cheating, it is important to understand why students do it.

This study looked at the behaviors of 803 university students in the Philippines and focused on three key influences: how students perceive cheating, whether they think it is morally wrong, and their motivations, such as academic pressure or lack of time. Machine learning methods were used to predict which students were likely to cheat based on these influences. Among the tested models, the Artificial Neural Network showed the strongest predictive results.

The findings show that when students believe an act is cheating and morally unacceptable, they are less likely to do it. But when they feel pressured or think the behavior is justified, cheating becomes more likely. The study emphasizes the need for clear policies, awareness, and ethical guidance in technology-enabled classrooms to support honesty and protect real learning.

This research advances the understanding of academic dishonesty in technology-integrated learning environments, an area that continues to expand with digital transformation and the rise of generative AI. By integrating Waltzer and Dahl’s Situated Decision-Making (SDM) model with machine learning techniques, the study not only confirms the theoretical importance of students’ perceptions, moral evaluations, and competing motivations but also demonstrates their predictive power in identifying students likely to engage in technology-enabled cheating. This predictive framework offers higher education institutions a practical and data-driven basis for designing targeted academic integrity interventions and proactive policy improvements. Ultimately, the study contributes to maintaining ethical standards and authentic learning within increasingly digital higher education systems.

Authors: Ben Oliver D. Tutor (General Education Department, School of Arts and Sciences, National University, Philippines) and Emerson R. Rico (Institute of Mathematical Sciences, College of Arts and Sciences, University of the Philippines Los Baños)

Read the full paper: https://doi.org/10.1007/s10805-025-09688-y

Image by Greta Hoffman from Pexels

Students are more likely to cheat under intense pressure or when they rationalize the behavior

Technology has become a big part of education, making learning more convenient and flexible. However, it has also made cheating easier. Students can now use multiple devices, messaging platforms, and even artificial intelligence tools to get answers during online tests or assignments. To help prevent cheating, it is important to understand why students do it.

This study looked at the behaviors of 803 university students in the Philippines and focused on three key influences: how students perceive cheating, whether they think it is morally wrong, and their motivations, such as academic pressure or lack of time. Machine learning methods were used to predict which students were likely to cheat based on these influences. Among the tested models, the Artificial Neural Network showed the strongest predictive results.

The findings show that when students believe an act is cheating and morally unacceptable, they are less likely to do it. But when they feel pressured or think the behavior is justified, cheating becomes more likely. The study emphasizes the need for clear policies, awareness, and ethical guidance in technology-enabled classrooms to support honesty and protect real learning.

This research advances the understanding of academic dishonesty in technology-integrated learning environments, an area that continues to expand with digital transformation and the rise of generative AI. By integrating Waltzer and Dahl’s Situated Decision-Making (SDM) model with machine learning techniques, the study not only confirms the theoretical importance of students’ perceptions, moral evaluations, and competing motivations but also demonstrates their predictive power in identifying students likely to engage in technology-enabled cheating. This predictive framework offers higher education institutions a practical and data-driven basis for designing targeted academic integrity interventions and proactive policy improvements. Ultimately, the study contributes to maintaining ethical standards and authentic learning within increasingly digital higher education systems.

Authors: Ben Oliver D. Tutor (General Education Department, School of Arts and Sciences, National University, Philippines) and Emerson R. Rico (Institute of Mathematical Sciences, College of Arts and Sciences, University of the Philippines Los Baños)

Read the full paper: https://doi.org/10.1007/s10805-025-09688-y

Image by Greta Hoffman from Pexels